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cs.RO2026

Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving

Chengzhen Meng, Pei Liu, Zhiyu Huang +2

Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of surrounding traffic. A fund…

cs.RO2026

FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving

Baoyun Wang, Zhuoren Li, Ran Yu +6

End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insufficiently addressed. In particu…

cs.RO20261 cited

Versatile Behavior Diffusion for Generalized Traffic Agent Simulation

Zhiyu Huang, Zixu Zhang, Ameya Vaidya +3

Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants…

cs.RO2024

Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Zhiyu Huang, Xinshuo Weng, Maximilian Igl +5

Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \tex…

cs.RO2024

Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving

Zhiyu Huang, Chen Tang, Chen Lv +2

Effective decision-making in autonomous driving relies on accurate inference of other traffic agents' future behaviors. To achieve this, we propose an online belief-update-based be…